Research on Demand Response Strategies for Photovoltaic – Energy Storage – Charging Integrated Power Stations Based on the Elasticity of Power Supply and Demand
DOI:
https://doi.org/10.13052/spee1048-5236.4428Keywords:
Supply – demand elasticity, demand response, integration of photovoltaics, energy storage and chargingAbstract
With the rapid development of renewable energy and electric vehicles, the application of integrated photovoltaic storage and charging power stations in smart grids is becoming increasingly widespread. Improving the supply and demand balance capacity and response speed of the power system has become a key issue that urgently needs to be solved at present. Based on the theory of power supply and demand elasticity, where the elasticity coefficient ranges from 0.2 to 1.8 and the time scale is 15 minutes, this paper proposes a demand response strategy for integrated photovoltaic storage and charging power stations. Firstly, by analyzing the characteristics of supply and demand elasticity changes, a dynamic model describing the interaction between photovoltaic power generation, energy storage systems and electric vehicle charging loads was constructed; Then, based on different assumptions of supply and demand elasticity, an optimized demand response strategy aimed at improving the economic benefits of the system and the stability of power supply and demand balance is proposed. The effectiveness of the demand response strategy under different supply and demand elasticity scenarios was verified through simulation analysis. The results show that reasonable adjustment of supply and demand elasticity can significantly improve the dispatching flexibility and response capacity of the power system, and promote the efficient utilization of green energy at the same time.
Downloads
References
Schwarz P M, Taylor T N, Birmingham M, et al. Industrial response to electricity real-time prices: short run andlong run[J]. Economic Inquiry, 2010, 40(4): 597–610.
Feehan J P. The long-run price elasticity of residential demand for electricity: results from a natural experiment [J]. UtilitiesPolicy, 2018, 51: 12–17.
Cialani C, Mortazavi R. Household and industrialelectricity demand in Europe[J]. Energy Policy, 2018, 122: 592–600.
Burke P J, Abayasekara A. The price elasticity of electricity demand in the United States: a three-dimensional analysis[J]. Energy Journal, 2018, 39(2): 123–145.
Zin A A B M, Moradi M. An experimental investigation of price elasticity in electricity markets using a response surface methodology[J]. Energy Efficiency, 2018, 12(3): 667–680.
Pielow A, Sioshansi R, Roberts M C. Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors [J]. Energy, 2012, 46(1): 533–540.
Kirschen D S, Strbac G, Cumperayot P, et al. Factoring the elasticity of demand in electricity prices[J]. IEEE Transactions on Power Systems, 2000, 15(2): 612–617.
Qin Zhenfang, Yue Shunmin, Yu Yixin, et al. Price elasticity matrix of demand in current retail power market[J]. Automation of Electric Power Systems, 2004, 28(5): 16–19.
Gao Yajing, Lyu Mengkuo, Liang Haifeng, et al. Power demand price elasticity matrix based on discrete attraction model [J]. Automation of Electric Power Systems, 2014, 38(13): 103–107.
Cochell J P. 24/7 hourly response to electricity real-time pricing with up to eight summers of experience[J]. Journal of Regulatory Economics, 2005, 27(3): 235–262.
Vivekananthan C, Mishra Y, Ledwich G, et al. Demand response for residential appliances via customer reward scheme[J]. IEEE Transactions on Smart Grid, 2014, 5(2): 809–820.
Cui Q, Wang X, Zhang Y. Residential appliances direct load control in real-time using cooperative game[J]. IEEE Transactions on Power Systems, 2016, 31(1): 226–233.
Luo Chunjian, Li Yaowang, Xu Hanping, et al. Influence of demand response uncertainty on day-ahead optimization dispatching[J]. Automation of Electric Power Systems, 2017, 41(5): 22–29.
Zadeh L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets & Systems, 1997, 90(2): 111–127.

https://aeeeuropeenergy.com/